code: def test__clf_with_no_proba_fail():
      X, y = iris_data()
      clf = OneRClassifier()
      clf.fit(X, y)
  
      x_ref = X[15]
  
      s = (
          "Your `model` does not support "
          "`predict_proba`. Set `y_desired_proba` "
          " to `None` to use `predict`instead."
      )
  
      assert_raises(
          AttributeError,
          s,
          create_counterfactual,
          x_ref,
          2,
          clf,
          X,
          1.0,
          100,
          123,
      )
  
file_path: mlxtend/mlxtend/evaluate/tests/test_counterfactual.py
imports: import numpy
  from sklearn.linear_model import LogisticRegression
  from mlxtend.classifier import OneRClassifier
  from mlxtend.data import iris_data
  from mlxtend.evaluate import create_counterfactual
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 4 for calling a global function with example usage: test__clf_with_no_proba_fail() without return types